Lead Data Integration Engineer

We are seeking an experienced and visionary Lead Data Integration Engineer to join our growing data solutions team. In this pivotal technical leadership role, you will act as a Tech Lead / Team Lead responsible for architecting, designing, and maintaining complex, enterprise-grade data pipelines. You will lead a high-performing engineering team (5+ engineers), shaping cloud-native and hybrid data solution architectures while establishing best practices.

Leveraging your extensive expertise in cloud infrastructure, database modeling, orchestration, and data security, you will transform raw data environments into robust, high-performance, and scalable analytical assets.

Responsibilities

Guide a team of data integration and ETL engineers, fostering a collaborative, innovation-driven culture. Manage sprint planning, resource allocation, and risk management Conduct regular code reviews and set up testing frameworks to ensure compliant, highly performant, and secure database code (SQL, Python, Scala) Act as a role model, championing modern software development practices (CI/CD, version control via Git, modular code design, automated alerting, and monitoring) Architect end-to-end data pipeline solutions using serverless frameworks or hybrid setups Establish robust pipeline scheduling and workflow orchestration using tools like Apache Airflow (Astronomer) or cloud-native schedulers to achieve high pipeline reliability (>99.

5%) and minimize troubleshooting times Design, model, and maintain scalable corporate data systems (OLAP, OLTP, Star/Snowflake schemas, Data Lakes, Lakehouses, and Data Meshes) Implement modern ingestion patterns, CDC methods, micro-batching, delta extracts, and routine partition maintenance Collaborate with product owners, enterprise architects, and business stakeholders to translate complex requirements into detailed technical specifications and data-flow diagrams Confidently present advanced technical architectures to C-level client executives, showcasing ROI, architectural benefits, and performance gains Requirements 5+ years of proven experience in data management, database design, migration, storage, and advanced data modeling Demonstrated experience leading, mentoring, and developing teams of data engineers (experience managing teams of 4–7 engineers is highly preferred) Deep hands-on experience with major ETL tools such as Talend, AWS Glue, Azure Data Factory, GCP Dataflow, or Apache Ni.

Fi Strong working experience with orchestration and scheduling engines, particularly Apache Airflow (Astronomer) Strong production coding skills in SQL, Python (Py. Spark, Pandas), Scala, SparkSQL, or Bash Extensive knowledge of at least one major cloud platform (AWS, Azure, GCP) with emphasis on big data warehousing engines (such as Redshift, Snowflake, Google Big.

Query, or Azure Synapse) Proven understanding of cloud security structures (identity and access management, data masking, and RLS) and compliance standards (GDPR, HIPAA, PI) Ability to write high-quality technical design specifications, mapping documentation, and data lineage reports Excellent verbal and written English communication (B2+/C1 level), capable of directly managing international clients and conducting presentations Nice to have Experience architecting fully serverless cloud data pipelines (e.

g., combining AWS S3, Athena, and AWS Lambda) Exposure to integrating machine learning models, computer vision, or generative AI workflows into data pipeline ingestion processes Professional certifications in Cloud Architecture (AWS, Azure, GCP), Talend, or Airflow